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Recent years have seen fast development in synthesizing realistic human facesusing AI technologies. Such fake faces can be weaponized to cause negativepersonal and social impact. In this work, we develop technologies to defendindividuals from becoming victims of recent AI synthesized fake videos bysabotaging would-be training data. This is achieved by disrupting deep neuralnetwork (DNN) based face detection method with specially designed imperceptibleadversarial perturbations to reduce the quality of the detected faces. Wedescribe attacking schemes under white-box, gray-box and black-box settings,each with decreasing information about the DNN based face detectors. Weempirically show the effectiveness of our methods in disruptingstate-of-the-art DNN based face detectors on several datasets.

Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations